Currently, there is no quantitative way to ascertain how an overcomplete signal representation describes a signal and its features using terms drawn from a dictionary. Though spars...
The richness of natural images makes the quest for optimal representations in image processing and computer vision challenging. The latter observation has not prevented the design...
We propose an ℓ1 criterion for dictionary learning for sparse signal representation. Instead of directly searching for the dictionary vectors, our dictionary learning approach i...
We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier p...
In a sparse-representation-based face recognition scheme, the desired dictionary should have good representational power (i.e., being able to span the subspace of all faces) while...